Electrophysiological analysis of healthy and dystrophic 3D bioengineered skeletal muscle tissues
Bibliographic record
Abstract
Abstract Recently, methods for creating three-dimensional (3D) human skeletal muscle tissues from myogenic cell lines have been reported. Bioengineered muscle tissues are contractile and respond to electrical and chemical stimulation. In this study we provide an electrophysiological analysis of healthy and dystrophic 3D bioengineered skeletal muscle tissues. We focus on Duchenne muscular dystrophy (DMD), a fatal muscle disorder involving the skeletal muscle system. The dystrophin gene, which when mutated causes DMD, encodes for the Dystrophin protein, which anchors the cytoskeletal network inside of a muscle cell to the extracellular matrix outside the cell. Here, we enlist a 3D in vitro model of DMD muscle tissue, to evaluate an understudied aspect of DMD, muscle cell electrical properties uncoupled from presynaptic neural inputs. Our data shows that electrophysiological aspects of DMD are replicated in the 3D bioengineered skeletal muscle tissue model. Furthermore, we test a block co-polymer, poloxamer 188, and demonstrate capacity for improving the membrane potential in DMD muscle.Therefore, this study serves as the baseline for a new in vitro method to examine potential therapies directed at muscular disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".